标签相关性指导区分标签特征学习,用于多标签胸部图像分类

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Kai Zhang¹, Wei Liang¹, Peng Cao²

¹Computer Science and Engineering, Northeastern University, Shenyang, China.

Computer methods and programs in biomedicine

|January 20, 2024

概括

本研究引入了一种新的多标签学习框架,通过有效学习和利用复杂的标签相关性来改进胸部X射线 (CXR) 分类. 拟议的方法显著提高了分类性能,优于现有的最先进的方法.

科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能

背景情况:

多标签胸部X射线 (CXR) 分类从了解标签关系中受益.
现有的方法很难有效地捕捉和利用这些复杂的标签相关性.

研究的目的:

为CXR图像分类提出一个新的多标签学习框架.
有效地学习和利用复杂的标签相关性,以提高性能.

主要方法:

全球标签相关性是使用自我注意机制捕获的.
图像级特征被分解成标签级特征以指导特征学习.
通过一致性约束和指导的对比损失,对标签级别特征进行端到端的增强.

主要成果:

拟议的方法在CheXpert数据集上获得了44.6%的F1平均得分和76.5%的AUC.
与最先进的方法相比,显示了7.7%的F1得分和1.3%的AUC改善.
通过三倍五倍交叉验证进行验证.

结论:

准确的学习和使用标签相关性导致更具歧视性的标签级特征.
拟议的框架在多标签CXR分类中实现了极具竞争力的性能.

关键词:

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